Characteristics of Soil Spectral Reflectance and Estimation of Soil Parameters in Fuxin Opencast Coal Mine
SUN Hong, LI Min-zan*, ZHAO Yong, LI Xiu-hua，LIANG Jing-xian, CHEN Ling
Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China
Abstract：The surface soil samples of Fuxin opencast coal overburden dumps were collected in the field and the spectral reflectance and characteristic parameters of the soil samples, such as moisture content, pH, electrical conductivity (EC), available potassium (K+) content, and soil organic matter content (SOM), were measured. The Analysis results indicated that the curves of the soil spectral reflectance decreased with increasing the laid years. The possible reasons were the influences of soil texture and color. Although the valleys of the spectral reflectance appeared at 1 420, 1 910 and 2 210 nm, they were not conspicuous on the lime soil and mixture soil reflectance curves at 1 420 and 2 210 nm. With discussing the spectral reflectance of different types of soil texture, it’s easy to find that the reflectance of fine grained soil was higher than the rough grained soil. Correlations between soil spectral reflectance and soil parameters were analyzed. The results showed that there was a positive relation between reflectance and pH, and correlation coefficient decreased with the wavelength increasing There was no relationship between spectral reflectance and EC, and negative relations were observed between spectral reflectance and soil parameters, K+ and SOM, respectively. A high correlation coefficient was found between spectral reflectance and SOM, and the highest correlation coefficient reached -0.76. The exponential correlation was found between sol spectral reflectance and soil moisture content to analyze all samples. According to different years and textures, more detail was described about the correlation between spectral reflectance of characteristic wavelength (1 910 and 1 943 nm) and soil moisture content. Meanwhile, the linear correlations were found under different conditions and higher correlation coefficients were obtained. In order to estimate SOM, five wavelengths (1 350, 1 602, 1 862, 2 160 and 2 227 nm) were selected based on principal component analysis to build a multiple linear regression model. The multiple correlation coefficient of calibration model (R2C) was 0.737 4, and the multiple correlation coefficient of validation (R2V) was 0.682 4. It indicated that the model was able to meet the needs of monitoring SOM in Fuxin opencast coal mine.
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